Data Manipulation in Python - Master Python, NumPy, and Pandas

Video description

Data science is quickly becoming one of the most promising careers in the twenty-first century. It is automated, program-driven, and analytical. As a result, it’s no surprise that the demand for data scientists has been expanding in the job market over the last few years.

We will begin with a quick refresher on Python fundamentals for beginners in this course. This is optional; if you’re already familiar with Python, skip to the next chapter.

Data science will be the topic of the next three sections. We will start with the essential Python libraries for data science, then go on to the fundamental NumPy properties, and lastly begin with mathematics and how to use it in data science.

You will learn about Python Pandas DataFrames and series after learning about data science. Following that, we will get down to business and begin data cleaning. Following that, we will learn how to use Python to visualize data and do data analysis on some sample datasets. Finally, we will cover the Time series in Python and learn how to work with and convert datasets to Time series.

By the end of this course, you will be able to execute data manipulation for data science in Python with ease.

What You Will Learn

  • A quick refresher to Python fundamentals
  • Learn to use Pandas for data analysis
  • Learn to work with numerical data in Python
  • Learn statistics and math with Python
  • Learn how to code in Jupyter Notebook
  • Learn how to install packages in Python


This course is open to students of all skill levels, and you will be able to succeed even if you have no prior programming or statistical knowledge.

About The Author

Meta Brains: Meta Brains is a team of passionate software developers and finance professionals. They provide professional training programs that combine their expertise in coding, finance, and Excel.

With a focus on the Metaverse, they aim to equip learners with the necessary skills to participate in the next computing revolution. Their inclusive approach ensures accessibility to everyone, fostering a community that collaboratively codes and builds the future of the Metaverse.

Publisher resources

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Table of contents

  1. Chapter 1 : Python Quick Refresher (Optional)
    1. Welcome to the course!
    2. Introduction to Python
    3. Setting up Python
    4. What is Jupyter?
    5. Anaconda Installation: Windows, Mac, and Ubuntu
    6. How to Implement Python in Jupyter?
    7. Managing Directories in Jupyter Notebook
    8. Input/Output
    9. Working with Different Datatypes
    10. Variables
    11. Arithmetic Operators
    12. Comparison Operators
    13. Logical Operators
    14. Conditional Statements
    15. Loops
    16. Sequences: Lists
    17. Sequences: Dictionaries
    18. Sequences: Tuples
    19. Functions: Built-in Functions
    20. Functions: User-Defined Functions
  2. Chapter 2 : Essential Python Libraries for Data Science
    1. Installing Libraries
    2. Importing Libraries
    3. Pandas Library for Data Science
    4. NumPy Library for Data Science
    5. Pandas versus NumPy
    6. Matplotlib Library for Data Science
    7. Seaborn Library for Data Science
  3. Chapter 3 : Fundamental NumPy Properties
    1. Introduction to NumPy Arrays
    2. Creating NumPy Arrays
    3. Indexing NumPy Arrays
    4. Array Shape
    5. Iterating Over NumPy Arrays
  4. Chapter 4 : Mathematics for Data Science
    1. Basic NumPy Arrays: zeros()
    2. Basic NumPy Arrays: ones()
    3. Basic NumPy Arrays: full()
    4. Adding a Scalar
    5. Subtracting a Scalar
    6. Multiplying by a Scalar
    7. Dividing by a Scalar
    8. Raise to a Power
    9. Transpose
    10. Element-Wise Addition
    11. Element-Wise Subtraction
    12. Element-Wise Multiplication
    13. Element-Wise Division
    14. Matrix Multiplication
    15. Statistics
  5. Chapter 5 : Python Pandas DataFrames and Series
    1. What is a Python Pandas DataFrame?
    2. What is a Python Pandas Series?
    3. DataFrame versus Series
    4. Creating a DataFrame Using Lists
    5. Creating a DataFrame Using a Dictionary
    6. Loading CSV Data into Python
    7. Changing the Index Column
    8. Inplace
    9. Examining the DataFrame: Head and Tail
    10. Statistical Summary of the DataFrame
    11. Slicing Rows Using Bracket Operators
    12. Indexing Columns Using Bracket Operators
    13. Boolean List
    14. Filtering Rows
    15. Filtering rows using ‘’ and ‘|’ Operators
    16. Filtering Data Using loc()
    17. Filtering Data Using iloc()
    18. Adding and Deleting Rows and Columns
    19. Sorting Values
    20. Exporting and Saving Pandas DataFrames
    21. Concatenating DataFrames
    22. Groupby()
  6. Chapter 6 : Data Cleaning
    1. Introduction to Data Cleaning
    2. Quality of Data
    3. Examples of Anomalies
    4. Median-based Anomaly Detection
    5. Mean-Based Anomaly Detection
    6. Z-Score-Based Anomaly Detection
    7. Interquartile Range for Anomaly Detection
    8. Dealing with Missing Values
    9. Regular Expressions
    10. Feature Scaling
  7. Chapter 7 : Data Visualization using Python
    1. Introduction
    2. Setting Up Matplotlib
    3. Plotting Line Plots using Matplotlib
    4. Title, Labels, and Legend
    5. Plotting Histograms
    6. Plotting Bar Charts
    7. Plotting Pie Charts
    8. Plotting Scatter Plots
    9. Plotting Log Plots
    10. Plotting Polar Plots
    11. Handling Dates
    12. Creating Multiple Subplots in One Figure
  8. Chapter 8 : Exploratory Data Analysis
    1. Introduction
    2. What is Exploratory Data Analysis?
    3. Univariate Analysis
    4. Univariate Analysis: Continuous Data
    5. Univariate Analysis: Categorical Data
    6. Bivariate Analysis: Continuous and Continuous
    7. Bivariate Analysis: Categorical and Categorical
    8. Bivariate Analysis: Continuous and Categorical
    9. Detecting Outliers
    10. Categorical Variable Transformation
  9. Chapter 9 : Time Series in Python
    1. Introduction to Time Series
    2. Getting Stock Data Using yfinance
    3. Converting a Dataset into Time Series
    4. Working with Time Series
    5. Time Series Data Visualization with Python

Product information

  • Title: Data Manipulation in Python - Master Python, NumPy, and Pandas
  • Author(s): Meta Brains
  • Release date: May 2022
  • Publisher(s): Packt Publishing
  • ISBN: 9781804614396